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2nd Workshop on AI for Climate and Conservation (AICC-2) at ECCV 2026
Event date:
Climate AI Nordics is glad to announce that the [2nd Workshop on AI for Climate and Conservation (AICC-)](https://sites.google.com/g.harvard.edu/aicc2eccv26/) has been accepted at [ECCV 2026](https://eccv.ecva.net/)! The AICC-2 workshop will take place in Malmö, Sweden, Sep 8th or 9th (TBD).
EarthShift: A new testbed for benchmarking robustnessGeospatial Foundation Models to real-world distribution shifts
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Webinar with Kelsey Doerksen, University of Cape Town and Arizona State University. Geospatial Foundation Models claim to offer powerful solutions to simplify and accelerate real-world problems, enabling the capabilities to monitor, analyze, and predict changes on our planet. Current Earth Observation benchmarks to quantify the performance of these models focus on measuring performance on diverse tasks and applications, typically measuring generalization in-distribution. However, when models are deployed, they must generalize to many out-of-distribution scenarios, such as new time periods, geographies, and sensors; and in many contexts, these models are brittle. We introduce EarthShift: the first public testbed for benchmarking robustness across multiple realistic distribution shifts encountered in remote sensing. EarthShift enables users to measure distributional robustness by comparing performance in- and out-of-distribution using datasets from paired data sources, temporal windows, geographic locations, and sensors. EarthShift provides a testbed to guide future work to create foundation models that are robust and reliable in real-world applications.
Postdoctoral Researcher: Satellite Remote Sensing of Carbon Cycle Sources and Sinks
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The Finnish Meteorological Institute is seeking a postdoctoral researcher to analyze and exploit satellite remote sensing datasets, including CO₂ and SIF observations, using AI methods for research on the carbon cycle, greenhouse gas emissions, and carbon sinks. This role involves developing advanced methods for data interpretation and source-sink estimation.
Postdoc in Production Management: AI-driven Sustainability and Resilience
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KTH Royal Institute of Technology is seeking a postdoctoral researcher in Production Management to investigate how AI and machine learning can support sustainable and resilient production systems, including circular manufacturing and green industrial transition.